diff --git a/Part1.ipynb b/Part1.ipynb
index 73a621b..a40c0fc 100644
--- a/Part1.ipynb
+++ b/Part1.ipynb
@@ -105,7 +105,7 @@
     {
      "data": {
       "text/plain": [
-       "<napari.viewer.Viewer at 0x21663894a00>"
+       "Viewer(axes=Axes(visible=False, labels=True, colored=True, dashed=False, arrows=True), camera=Camera(center=(0.0, 639.5, 958.5), zoom=0.2859415753781951, angles=(0.0, 0.0, 90.0), interactive=True), cursor=Cursor(position=(0.0, 0.0), scaled=True, size=10, style='standard'), dims=Dims(ndim=2, ndisplay=2, last_used=1, range=((0.0, 1279.0, 1.0), (0.0, 1917.0, 1.0)), current_step=(0, 0), order=(0, 1), axis_labels=('0', '1')), grid=GridCanvas(enabled=False, stride=1, shape=(-1, -1)), layers=[<Image layer 'input_training' at 0x20a86a790a0>, <Labels layer 'target_training' at 0x20a86a79b50>], scale_bar=ScaleBar(visible=False, colored=False, ticks=True, position='bottom_right'), active_layer=<Labels layer 'target_training' at 0x20a86a79b50>, help='enter paint or fill mode to edit labels', status='Ready', theme='dark', title='napari', mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x0000020A80C60790>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, keymap={'T': <function show_input_target_pair_napari.<locals>.next_batch_training at 0x0000020A8FDD9670>})"
       ]
      },
      "execution_count": 3,
@@ -176,9 +176,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Viewer(axes=Axes(visible=False, labels=True, colored=True, dashed=False, arrows=True), camera=Camera(center=(0.0, 639.5, 958.5), zoom=0.2859415753781951, angles=(0.0, 0.0, 90.0), interactive=True), cursor=Cursor(position=(0.0, 0.0), scaled=True, size=10, style='standard'), dims=Dims(ndim=2, ndisplay=2, last_used=1, range=((0.0, 1279.0, 1.0), (0.0, 1917.0, 1.0)), current_step=(0, 0), order=(0, 1), axis_labels=('0', '1')), grid=GridCanvas(enabled=False, stride=1, shape=(-1, -1)), layers=[<Image layer 'input_training' at 0x20a97119760>, <Labels layer 'target_training' at 0x20a97060be0>], scale_bar=ScaleBar(visible=False, colored=False, ticks=True, position='bottom_right'), active_layer=<Labels layer 'target_training' at 0x20a97060be0>, help='enter paint or fill mode to edit labels', status='Ready', theme='dark', title='napari', mouse_move_callbacks=[], mouse_drag_callbacks=[], mouse_wheel_callbacks=[<function dims_scroll at 0x0000020A80C60790>], _persisted_mouse_event={}, _mouse_drag_gen={}, _mouse_wheel_gen={}, keymap={'T': <function show_input_target_pair_napari.<locals>.next_batch_training at 0x0000020A9D207AF0>, 'V': <function show_input_target_pair_napari.<locals>.next_batch_validation at 0x0000020A9D207B80>})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%gui qt\n",
     "from visual import Input_Target_Pair_Generator\n",
@@ -205,9 +216,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/Part2.ipynb b/Part2.ipynb
index 1761200..e3cd664 100644
--- a/Part2.ipynb
+++ b/Part2.ipynb
@@ -41,101 +41,90 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "==========================================================================================\n",
-      "Layer (type:depth-idx)                   Output Shape              Param #\n",
-      "==========================================================================================\n",
-      "├─ModuleList: 1                          []                        --\n",
-      "|    └─DownBlock: 2-1                    [-1, 32, 256, 256]        --\n",
-      "|    |    └─Conv2d: 3-1                  [-1, 32, 512, 512]        320\n",
-      "|    |    └─ReLU: 3-2                    [-1, 32, 512, 512]        --\n",
-      "|    |    └─BatchNorm2d: 3-3             [-1, 32, 512, 512]        64\n",
-      "|    |    └─Conv2d: 3-4                  [-1, 32, 512, 512]        9,248\n",
-      "|    |    └─ReLU: 3-5                    [-1, 32, 512, 512]        --\n",
-      "|    |    └─BatchNorm2d: 3-6             [-1, 32, 512, 512]        64\n",
-      "|    |    └─MaxPool2d: 3-7               [-1, 32, 256, 256]        --\n",
-      "|    └─DownBlock: 2-2                    [-1, 64, 128, 128]        --\n",
-      "|    |    └─Conv2d: 3-8                  [-1, 64, 256, 256]        18,496\n",
-      "|    |    └─ReLU: 3-9                    [-1, 64, 256, 256]        --\n",
-      "|    |    └─BatchNorm2d: 3-10            [-1, 64, 256, 256]        128\n",
-      "|    |    └─Conv2d: 3-11                 [-1, 64, 256, 256]        36,928\n",
-      "|    |    └─ReLU: 3-12                   [-1, 64, 256, 256]        --\n",
-      "|    |    └─BatchNorm2d: 3-13            [-1, 64, 256, 256]        128\n",
-      "|    |    └─MaxPool2d: 3-14              [-1, 64, 128, 128]        --\n",
-      "|    └─DownBlock: 2-3                    [-1, 128, 64, 64]         --\n",
-      "|    |    └─Conv2d: 3-15                 [-1, 128, 128, 128]       73,856\n",
-      "|    |    └─ReLU: 3-16                   [-1, 128, 128, 128]       --\n",
-      "|    |    └─BatchNorm2d: 3-17            [-1, 128, 128, 128]       256\n",
-      "|    |    └─Conv2d: 3-18                 [-1, 128, 128, 128]       147,584\n",
-      "|    |    └─ReLU: 3-19                   [-1, 128, 128, 128]       --\n",
-      "|    |    └─BatchNorm2d: 3-20            [-1, 128, 128, 128]       256\n",
-      "|    |    └─MaxPool2d: 3-21              [-1, 128, 64, 64]         --\n",
-      "|    └─DownBlock: 2-4                    [-1, 256, 64, 64]         --\n",
-      "|    |    └─Conv2d: 3-22                 [-1, 256, 64, 64]         295,168\n",
-      "|    |    └─ReLU: 3-23                   [-1, 256, 64, 64]         --\n",
-      "|    |    └─BatchNorm2d: 3-24            [-1, 256, 64, 64]         512\n",
-      "|    |    └─Conv2d: 3-25                 [-1, 256, 64, 64]         590,080\n",
-      "|    |    └─ReLU: 3-26                   [-1, 256, 64, 64]         --\n",
-      "|    |    └─BatchNorm2d: 3-27            [-1, 256, 64, 64]         512\n",
-      "├─ModuleList: 1                          []                        --\n",
-      "|    └─UpBlock: 2-5                      [-1, 128, 128, 128]       --\n",
-      "|    |    └─ConvTranspose2d: 3-28        [-1, 128, 128, 128]       131,200\n",
-      "|    |    └─ReLU: 3-29                   [-1, 128, 128, 128]       --\n",
-      "|    |    └─BatchNorm2d: 3-30            [-1, 128, 128, 128]       256\n",
-      "|    |    └─Concatenate: 3-31            [-1, 256, 128, 128]       --\n",
-      "|    |    └─Conv2d: 3-32                 [-1, 128, 128, 128]       295,040\n",
-      "|    |    └─ReLU: 3-33                   [-1, 128, 128, 128]       --\n",
-      "|    |    └─BatchNorm2d: 3-34            [-1, 128, 128, 128]       256\n",
-      "|    |    └─Conv2d: 3-35                 [-1, 128, 128, 128]       147,584\n",
-      "|    |    └─ReLU: 3-36                   [-1, 128, 128, 128]       --\n",
-      "|    |    └─BatchNorm2d: 3-37            [-1, 128, 128, 128]       256\n",
-      "|    └─UpBlock: 2-6                      [-1, 64, 256, 256]        --\n",
-      "|    |    └─ConvTranspose2d: 3-38        [-1, 64, 256, 256]        32,832\n",
-      "|    |    └─ReLU: 3-39                   [-1, 64, 256, 256]        --\n",
-      "|    |    └─BatchNorm2d: 3-40            [-1, 64, 256, 256]        128\n",
-      "|    |    └─Concatenate: 3-41            [-1, 128, 256, 256]       --\n",
-      "|    |    └─Conv2d: 3-42                 [-1, 64, 256, 256]        73,792\n",
-      "|    |    └─ReLU: 3-43                   [-1, 64, 256, 256]        --\n",
-      "|    |    └─BatchNorm2d: 3-44            [-1, 64, 256, 256]        128\n",
-      "|    |    └─Conv2d: 3-45                 [-1, 64, 256, 256]        36,928\n",
-      "|    |    └─ReLU: 3-46                   [-1, 64, 256, 256]        --\n",
-      "|    |    └─BatchNorm2d: 3-47            [-1, 64, 256, 256]        128\n",
-      "|    └─UpBlock: 2-7                      [-1, 32, 512, 512]        --\n",
-      "|    |    └─ConvTranspose2d: 3-48        [-1, 32, 512, 512]        8,224\n",
-      "|    |    └─ReLU: 3-49                   [-1, 32, 512, 512]        --\n",
-      "|    |    └─BatchNorm2d: 3-50            [-1, 32, 512, 512]        64\n",
-      "|    |    └─Concatenate: 3-51            [-1, 64, 512, 512]        --\n",
-      "|    |    └─Conv2d: 3-52                 [-1, 32, 512, 512]        18,464\n",
-      "|    |    └─ReLU: 3-53                   [-1, 32, 512, 512]        --\n",
-      "|    |    └─BatchNorm2d: 3-54            [-1, 32, 512, 512]        64\n",
-      "|    |    └─Conv2d: 3-55                 [-1, 32, 512, 512]        9,248\n",
-      "|    |    └─ReLU: 3-56                   [-1, 32, 512, 512]        --\n",
-      "|    |    └─BatchNorm2d: 3-57            [-1, 32, 512, 512]        64\n",
-      "├─Conv2d: 1-1                            [-1, 2, 512, 512]         66\n",
-      "==========================================================================================\n",
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1         [-1, 32, 512, 512]             320\n",
+      "              ReLU-2         [-1, 32, 512, 512]               0\n",
+      "       BatchNorm2d-3         [-1, 32, 512, 512]              64\n",
+      "            Conv2d-4         [-1, 32, 512, 512]           9,248\n",
+      "              ReLU-5         [-1, 32, 512, 512]               0\n",
+      "       BatchNorm2d-6         [-1, 32, 512, 512]              64\n",
+      "         MaxPool2d-7         [-1, 32, 256, 256]               0\n",
+      "         DownBlock-8  [[-1, 32, 256, 256], [-1, 32, 512, 512]]               0\n",
+      "            Conv2d-9         [-1, 64, 256, 256]          18,496\n",
+      "             ReLU-10         [-1, 64, 256, 256]               0\n",
+      "      BatchNorm2d-11         [-1, 64, 256, 256]             128\n",
+      "           Conv2d-12         [-1, 64, 256, 256]          36,928\n",
+      "             ReLU-13         [-1, 64, 256, 256]               0\n",
+      "      BatchNorm2d-14         [-1, 64, 256, 256]             128\n",
+      "        MaxPool2d-15         [-1, 64, 128, 128]               0\n",
+      "        DownBlock-16  [[-1, 64, 128, 128], [-1, 64, 256, 256]]               0\n",
+      "           Conv2d-17        [-1, 128, 128, 128]          73,856\n",
+      "             ReLU-18        [-1, 128, 128, 128]               0\n",
+      "      BatchNorm2d-19        [-1, 128, 128, 128]             256\n",
+      "           Conv2d-20        [-1, 128, 128, 128]         147,584\n",
+      "             ReLU-21        [-1, 128, 128, 128]               0\n",
+      "      BatchNorm2d-22        [-1, 128, 128, 128]             256\n",
+      "        MaxPool2d-23          [-1, 128, 64, 64]               0\n",
+      "        DownBlock-24  [[-1, 128, 64, 64], [-1, 128, 128, 128]]               0\n",
+      "           Conv2d-25          [-1, 256, 64, 64]         295,168\n",
+      "             ReLU-26          [-1, 256, 64, 64]               0\n",
+      "      BatchNorm2d-27          [-1, 256, 64, 64]             512\n",
+      "           Conv2d-28          [-1, 256, 64, 64]         590,080\n",
+      "             ReLU-29          [-1, 256, 64, 64]               0\n",
+      "      BatchNorm2d-30          [-1, 256, 64, 64]             512\n",
+      "        DownBlock-31  [[-1, 256, 64, 64], [-1, 256, 64, 64]]               0\n",
+      "  ConvTranspose2d-32        [-1, 128, 128, 128]         131,200\n",
+      "             ReLU-33        [-1, 128, 128, 128]               0\n",
+      "      BatchNorm2d-34        [-1, 128, 128, 128]             256\n",
+      "      Concatenate-35        [-1, 256, 128, 128]               0\n",
+      "           Conv2d-36        [-1, 128, 128, 128]         295,040\n",
+      "             ReLU-37        [-1, 128, 128, 128]               0\n",
+      "      BatchNorm2d-38        [-1, 128, 128, 128]             256\n",
+      "           Conv2d-39        [-1, 128, 128, 128]         147,584\n",
+      "             ReLU-40        [-1, 128, 128, 128]               0\n",
+      "      BatchNorm2d-41        [-1, 128, 128, 128]             256\n",
+      "          UpBlock-42        [-1, 128, 128, 128]               0\n",
+      "  ConvTranspose2d-43         [-1, 64, 256, 256]          32,832\n",
+      "             ReLU-44         [-1, 64, 256, 256]               0\n",
+      "      BatchNorm2d-45         [-1, 64, 256, 256]             128\n",
+      "      Concatenate-46        [-1, 128, 256, 256]               0\n",
+      "           Conv2d-47         [-1, 64, 256, 256]          73,792\n",
+      "             ReLU-48         [-1, 64, 256, 256]               0\n",
+      "      BatchNorm2d-49         [-1, 64, 256, 256]             128\n",
+      "           Conv2d-50         [-1, 64, 256, 256]          36,928\n",
+      "             ReLU-51         [-1, 64, 256, 256]               0\n",
+      "      BatchNorm2d-52         [-1, 64, 256, 256]             128\n",
+      "          UpBlock-53         [-1, 64, 256, 256]               0\n",
+      "  ConvTranspose2d-54         [-1, 32, 512, 512]           8,224\n",
+      "             ReLU-55         [-1, 32, 512, 512]               0\n",
+      "      BatchNorm2d-56         [-1, 32, 512, 512]              64\n",
+      "      Concatenate-57         [-1, 64, 512, 512]               0\n",
+      "           Conv2d-58         [-1, 32, 512, 512]          18,464\n",
+      "             ReLU-59         [-1, 32, 512, 512]               0\n",
+      "      BatchNorm2d-60         [-1, 32, 512, 512]              64\n",
+      "           Conv2d-61         [-1, 32, 512, 512]           9,248\n",
+      "             ReLU-62         [-1, 32, 512, 512]               0\n",
+      "      BatchNorm2d-63         [-1, 32, 512, 512]              64\n",
+      "          UpBlock-64         [-1, 32, 512, 512]               0\n",
+      "           Conv2d-65          [-1, 2, 512, 512]              66\n",
+      "================================================================\n",
       "Total params: 1,928,322\n",
       "Trainable params: 1,928,322\n",
       "Non-trainable params: 0\n",
-      "Total mult-adds (M): -14267.05\n",
-      "==========================================================================================\n",
+      "----------------------------------------------------------------\n",
       "Input size (MB): 1.00\n",
-      "Forward/backward pass size (MB): 1156.00\n",
+      "Forward/backward pass size (MB): 2096.00\n",
       "Params size (MB): 7.36\n",
-      "Estimated Total Size (MB): 1164.36\n",
-      "==========================================================================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\ProgramData\\Miniconda3\\envs\\test_env\\lib\\site-packages\\torchsummary\\layer_info.py:122: RuntimeWarning: overflow encountered in long_scalars\n",
-      "  self.macs += int(param.nelement() * np.prod(self.output_size[2:]))\n"
+      "Estimated Total Size (MB): 2104.36\n",
+      "----------------------------------------------------------------\n"
      ]
     }
    ],
    "source": [
     "from torchsummary import summary\n",
-    "summary = summary(model, (1, 512, 512))"
+    "summary = summary(model, (1, 512, 512), device='cpu')"
    ]
   },
   {
@@ -183,8 +172,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{256: [256, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0, 2.0],\n",
+       " 384: [384, 192.0, 96.0, 48.0, 24.0, 12.0, 6.0, 3.0],\n",
+       " 512: [512, 256.0, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "low = 10\n",
     "high = 512\n",
@@ -203,17 +214,11 @@
     "    return possible_shapes\n",
     "possible_shapes = compute_possible_shapes(low, high, depth)\n",
     "possible_shapes"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -224,7 +229,7 @@
        " 512: [512, 256.0, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0]}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -266,9 +271,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/Part3.ipynb b/Part3.ipynb
index 54e84c3..399d66f 100644
--- a/Part3.ipynb
+++ b/Part3.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,20 +99,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<napari.viewer.Viewer at 0x1f27311f220>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%gui qt\n",
     "from visual import Input_Target_Pair_Generator\n",
@@ -125,143 +114,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dfc20e6006464aa9a1ff59fc1373560d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Progress'), FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# device\n",
     "if torch.cuda.is_available():\n",
@@ -298,7 +153,7 @@
     "                  notebook=True)\n",
     "\n",
     "# start training\n",
-    "training_losses, validation_losses, lr_rates = trainer.run_trainer()"
+    "training_losses, validation_losses, lr_rates = trainer.run_trainer(notebook=True)"
    ]
   },
   {
@@ -310,52 +165,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "31768e4b3af64942adb535c3071bf783",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Caching'), FloatProgress(value=0.0, max=76.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "efe7a2a8858f40bfb0e3045c9069a578",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Caching'), FloatProgress(value=0.0, max=20.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Imports\n",
     "import pathlib\n",
@@ -445,20 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<napari.viewer.Viewer at 0x1f2174b1d30>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%gui qt\n",
     "from visual import Input_Target_Pair_Generator\n",
@@ -471,311 +272,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "071a9c2ec2ad4d58831ddf5f6d84fac4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Progress'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "version_minor": 0
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-    {
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-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
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-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# device\n",
     "if torch.cuda.is_available():\n",
@@ -824,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -916,2831 +415,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(HTML(value='Progress'), FloatProgress(value=0.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-       "version_minor": 0
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-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
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-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
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-    {
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-     "output_type": "display_data"
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-    {
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-       "version_minor": 0
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-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=38.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Validation'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# device\n",
     "if torch.cuda.is_available():\n",
@@ -3790,32 +467,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# save the model\n",
-    "model_name =  'carvana_model.pt'\n",
-    "torch.save(model.state_dict(), pathlib.Path.cwd() / model_name)"
-   ],
    "metadata": {
     "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Learning rate finder"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
@@ -3834,24 +490,6 @@
     "# Learning rate finder"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save the model\n",
-    "model_name =  'carvana_model.pt'\n",
-    "torch.save(model.state_dict(), pathlib.Path.cwd() / model_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Learning rate finder"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3935,9 +573,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/Part4.ipynb b/Part4.ipynb
index 18a6aa8..623afab 100644
--- a/Part4.ipynb
+++ b/Part4.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -123,9 +123,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\programdata\\miniconda3_3.8\\envs\\pytorch_env\\lib\\site-packages\\napari\\layers\\labels\\labels.py:382: UserWarning: Float dtypes are not supported for Labels layers. Converting data to integers...\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "%gui qt\n",
     "import napari\n",
@@ -155,9 +164,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/inference.py b/inference.py
index bc871ff..eb8f1ae 100644
--- a/inference.py
+++ b/inference.py
@@ -16,4 +16,4 @@ def predict(img,
     out_softmax = torch.softmax(out, dim=1)  # perform softmax on outputs
     result = postprocess(out_softmax)  # postprocess outputs
 
-    return result
\ No newline at end of file
+    return result
diff --git a/lr_rate_finder.py b/lr_rate_finder.py
index 0e5ed7d..1fae978 100644
--- a/lr_rate_finder.py
+++ b/lr_rate_finder.py
@@ -96,4 +96,4 @@ def reset(self):
         """
         self.model.load_state_dict(self._model_init)
         self.optimizer.load_state_dict(self._opt_init)
-        print('Model and optimizer in initial state.')
\ No newline at end of file
+        print('Model and optimizer in initial state.')
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..3d2add7
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,134 @@
+alabaster==0.7.12
+albumentations==0.5.2
+anyio==2.2.0
+appdirs==1.4.4
+argon2-cffi==20.1.0
+async-generator==1.10
+attrs==20.3.0
+Babel==2.9.0
+backcall==0.2.0
+bleach==3.3.0
+cachey==0.2.1
+certifi==2020.12.5
+cffi==1.14.5
+chardet==4.0.0
+cloudpickle==1.6.0
+colorama==0.4.4
+cycler==0.10.0
+dask==2021.4.0
+decorator==4.4.2
+defusedxml==0.7.1
+docstring-parser==0.7.3
+docutils==0.16
+entrypoints==0.3
+freetype-py==2.2.0
+fsspec==0.9.0
+HeapDict==1.0.1
+idna==2.10
+imageio==2.9.0
+imagesize==1.2.0
+imgaug==0.4.0
+ipykernel==5.5.3
+ipython==7.22.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==2.11.3
+joblib==1.0.1
+json5==0.9.5
+jsonschema==3.2.0
+jupyter-client==6.2.0
+jupyter-core==4.7.1
+jupyter-packaging==0.7.12
+jupyter-server==1.6.0
+jupyterlab==3.0.13
+jupyterlab-pygments==0.1.2
+jupyterlab-server==2.4.0
+jupyterlab-widgets==1.0.0
+kiwisolver==1.3.1
+locket==0.2.1
+magicgui==0.2.9
+MarkupSafe==1.1.1
+matplotlib==3.4.1
+mistune==0.8.4
+napari==0.4.7
+napari-console==0.0.3
+napari-plugin-engine==0.1.9
+napari-svg==0.1.4
+nbclassic==0.2.7
+nbclient==0.5.3
+nbconvert==6.0.7
+nbformat==5.1.3
+nest-asyncio==1.5.1
+networkx==2.5.1
+notebook==6.3.0
+numpy==1.20.2
+numpydoc==1.1.0
+opencv-python==4.5.1.48
+opencv-python-headless==4.5.1.48
+packaging==20.9
+pandas==1.2.4
+pandocfilters==1.4.3
+parso==0.8.2
+partd==1.2.0
+pickleshare==0.7.5
+Pillow==8.2.0
+prometheus-client==0.10.1
+prompt-toolkit==3.0.18
+psutil==5.8.0
+pycparser==2.20
+pydantic==1.8.1
+Pygments==2.8.1
+PyOpenGL==3.1.5
+pyparsing==2.4.7
+pypiwin32==223
+PyQt5==5.15.4
+PyQt5-Qt5==5.15.2
+PyQt5-sip==12.8.1
+pyrsistent==0.17.3
+python-dateutil==2.8.1
+pytz==2021.1
+PyWavelets==1.1.1
+pywin32==300
+pywinpty==0.5.7
+PyYAML==5.4.1
+pyzmq==22.0.3
+qtconsole==5.0.3
+QtPy==1.9.0
+requests==2.25.1
+scikit-image==0.18.1
+scikit-learn==0.24.1
+scipy==1.6.2
+Send2Trash==1.5.0
+Shapely==1.7.1
+six==1.15.0
+sklearn==0.0
+sniffio==1.2.0
+snowballstemmer==2.1.0
+Sphinx==3.5.4
+sphinxcontrib-applehelp==1.0.2
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==1.0.3
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.4
+terminado==0.9.4
+testpath==0.4.4
+threadpoolctl==2.1.0
+tifffile==2021.4.8
+toolz==0.11.1
+torch==1.8.1+cu111
+torchaudio==0.8.1
+torchsummary==1.5.1
+torchvision==0.9.1+cu111
+tornado==6.1
+tqdm==4.60.0
+traitlets==5.0.5
+typing-extensions==3.7.4.3
+urllib3==1.26.4
+vispy==0.6.6
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1
+wincertstore==0.2
+wrapt==1.12.1
diff --git a/trainer.py b/trainer.py
index 7725c72..d4bc1f0 100644
--- a/trainer.py
+++ b/trainer.py
@@ -112,4 +112,4 @@ def _validate(self):
 
         self.validation_loss.append(np.mean(valid_losses))
 
-        batch_iter.close()
\ No newline at end of file
+        batch_iter.close()
diff --git a/unet.py b/unet.py
index e7d7b44..6b2d766 100644
--- a/unet.py
+++ b/unet.py
@@ -390,4 +390,4 @@ def forward(self, x: torch.tensor):
     def __repr__(self):
         attributes = {attr_key: self.__dict__[attr_key] for attr_key in self.__dict__.keys() if '_' not in attr_key[0] and 'training' not in attr_key}
         d = {self.__class__.__name__: attributes}
-        return f'{d}'
\ No newline at end of file
+        return f'{d}'